Machine Learning A-Z: Hands-On Python & R In Data Science 4.5 (123,398 ratings) Course Ratings are calculated from individual students' ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately. Then this course is for you! This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.
Half of the top ten most reliable cars in their category sold in the UK are from Japanese brands, a new owner-satisfaction report from What Car? has found. But while it was good news for Japan, it was not so great for UK car making, with the Range Rover Sport, Jaguar XE and the British-built Nissan Qashqai coming bottom of their respective categories. Overall, nearly a third (30 per cent) of motorists with cars of up to three years old suffered at least one fault with their car over the previous 12 months, the report said. Britain's hugely-popular Range Rover Sport diesel emerged as one of the worst performers with 60 per cent of vehicles suffering issues, the lowest overall breakdown reliability rating of any car reviewed and some examples being'off the road for a week' while they were fixed. That stood in contrast to the astonishing 100 per cent reliability scores racked up by four cars: the Honda Jazz, Toyota Aygo, Lexus CT200h and Audi A3, all of which owners said had been fault-free throughout the year.
Rating platforms enable large-scale collection of user opinion about items (products, other users, etc.). However, many untrustworthy users give fraudulent ratings for excessive monetary gains. In the paper, we present FairJudge, a system to identify such fraudulent users. We propose three metrics: (i) the fairness of a user that quantifies how trustworthy the user is in rating the products, (ii) the reliability of a rating that measures how reliable the rating is, and (iii) the goodness of a product that measures the quality of the product. Intuitively, a user is fair if it provides reliable ratings that are close to the goodness of the product. We formulate a mutually recursive definition of these metrics, and further address cold start problems and incorporate behavioral properties of users and products in the formulation. We propose an iterative algorithm, FairJudge, to predict the values of the three metrics. We prove that FairJudge is guaranteed to converge in a bounded number of iterations, with linear time complexity. By conducting five different experiments on five rating platforms, we show that FairJudge significantly outperforms nine existing algorithms in predicting fair and unfair users. We reported the 100 most unfair users in the Flipkart network to their review fraud investigators, and 80 users were correctly identified (80% accuracy). The FairJudge algorithm is already being deployed at Flipkart.
Government agencies are struggling to modernize and digitize their IT infrastructures and workflows. Surveys show that these agencies want to leverage innovative new technologies and empower mobile workers, but they're concerned about security and risk management, not to mention budgetary constraints. Digital workspace environments help agencies embrace cloud and mobile while preserving legacy investments, ensuring continuity of operations, and maintaining tight security and data protection. This infographic explores some of the top IT priorities and challenges identified by state and federal offices, then explains how digital workspaces deliver flexibility, reliability and productivity without compromises.